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A survey on multimodal large language models.

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Multimodal large language models (MLLMs) show emergent capabilities, advancing towards artificial general intelligence. This paper surveys recent MLLM progress, covering architectures, training, and future research directions.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Natural Language Processing

Background:

  • Multimodal Large Language Models (MLLMs), exemplified by GPT-4V, are a rapidly advancing research area.
  • MLLMs leverage Large Language Models (LLMs) for complex multimodal tasks, exhibiting emergent capabilities beyond traditional methods.
  • The development of MLLMs is accelerating, with significant contributions from both academia and industry.

Purpose of the Study:

  • To provide a comprehensive overview and summary of recent advancements in Multimodal Large Language Models (MLLMs).
  • To delineate the fundamental formulation, architecture, training strategies, data, and evaluation metrics of MLLMs.
  • To explore extensions and challenges within the MLLM domain, including multimodality, multilingualism, and advanced reasoning techniques.

Main Methods:

  • Systematic review and summarization of current MLLM research.
  • Analysis of MLLM architectures, training methodologies, and datasets.
  • Exploration of emerging research topics such as enhanced granularity, modality support, language capabilities, and scenario applications.
  • Investigation of multimodal hallucination and advanced techniques like in-context learning, chain-of-thought, and LLM-aided visual reasoning.

Main Results:

  • Recent MLLMs demonstrate surprising emergent abilities, including image-based story generation and optical character recognition-free mathematical reasoning.
  • Significant progress has been made in developing MLLMs that rival or surpass existing benchmarks like GPT-4V.
  • The field is rapidly evolving, with ongoing efforts to enhance MLLM capabilities across various dimensions.

Conclusions:

  • MLLMs represent a significant step towards artificial general intelligence due to their emergent capabilities.
  • This survey provides a structured understanding of the MLLM landscape, highlighting key concepts and research trajectories.
  • Future research should address current challenges and explore promising directions to further advance MLLM technology.